A Microscopic Model of Vehicle CO₂ Emissions Based on Deep Learning--A Spatiotemporal Analysis of Taxicabs in Wuhan, China

It is important to assess environmental impact of intelligent transportation systems, and hence developing a vehicle emission model with high accuracy has been a long-standing topic in transportation research. However, current vehicle emission models are either overly simple using average speed, resulting in low estimation accuracy, or they are too complicated requiring excessive inputs, relying on too much prior knowledge. In this study, we develop and evaluate a deep learning-based vehicle emission model (DL-VEM) to estimate the instantaneous CO ₂ emissions of taxicabs. First, we examine the correlation between observed emissions and vehicle driving condition data collected in a PEMS experiment. Then, an end-to-end deep learning structure is developed to model patterns of vehicle emissions. Specifically, LSTM networks are used to learn temporal dependencies of historical driving patterns, and fully connected networks are employed to extract deep features of current driving behaviors and external environment. Our model aggregates the outputs of these networks using different learnable weights. Experiments were conducted in Wuhan, China, where our model was trained and validated using observed datasets. Compared with the state-of-the-art models, our model achieved higher accuracy in estimating CO ₂ emissions. Thereafter, it was applied to a taxicab trajectory dataset in one day, and spatiotemporal patterns of CO ₂ emissions were presented using different fuel types. Importantly, we find that an increment of 24.94% emissions can be expected if petrol instead of compressed natural gas was used by each taxicab in Wuhan.

Automated summary

Assessing the environmental impact of intelligent transportation systems is important, and developing an accurate vehicle emission model has long been a topic of research. Current models are either too simple with low accuracy or too complex with excessive inputs. In this study, a deep learning-based vehicle emission model was developed and evaluated, achieving higher accuracy in estimating CO ₂ emissions compared to state-of-the-art models. The model was applied to a taxi dataset, revealing the spatiotemporal patterns of emissions with different fuel types.